A Novel Algorithm for Solving the Assembly Line Balancing Type I Problem
Why this work is in the frame
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Bibliographic record
Abstract
This paper presents a new modeling approach called Progressive Modeling (PM) and demonstrates it by solving the Assembly Line Balancing Type I Problem. PM introduces some new concepts that make the modeling process of large-scale complex industrial problems more systematic and their solution algorithms much faster and easily maintained. In the context of SALBP-I, PM introduces a component model to deploy the problem logic and its solution algorithm into several interacting components. The problem is represented as an object-oriented graph G (V, E, W) of vertices, edges, and workstations which enables problem solutions to start anywhere. The novel representation relaxes the only forward and backward tracking approach used in the assembly line balancing literature. A set of well-reported problems in the literature are reported and solved. The paper concludes by demonstrating the efficiency of the new modeling approach and future extensions.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it